function approximation - Robuta Search

https://openreview.net/forum?id=VwUTz2pOnD Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist... Reinforcement Learning (RL) utilizing kernel ridge regression to predict the expected value function represents a powerful method with great representational... kernel based function approximation reinforcement learning https://openreview.net/forum?id=JQR8OCptcG Improving Set Function Approximation with Quasi-Arithmetic Neural Networks | OpenReview Sets represent a fundamental abstraction across many types of data. To handle the unordered nature of set-structured data, models such as DeepSets and PointNet... set function neural networks improving approximation quasi https://openreview.net/forum?id=KmJo2sqppO Non-stationary Reinforcement Learning under General Function Approximation | OpenReview General function approximation is a powerful tool to handle large state and action spaces in a broad range of reinforcement learning (RL) scenarios. However,... non stationary reinforcement learning function approximation general openreview https://openreview.net/forum?id=RqCC_00Bg7V Blending MPC & Value Function Approximation for Efficient Reinforcement Learning | OpenReview Model-Predictive Control (MPC) is a powerful tool for controlling complex, real-world systems that uses a model to make predictions about future behavior. For... value function reinforcement learning blending mpc approximation https://openreview.net/forum?id=rylt7mFU8S Additive function approximation in the brain | OpenReview We advocate for random features as a theory of biological neural networks, focusing on sparsely connected networks in the brain additive function approximation openreview https://openreview.net/forum?id=NvaZn3uwzJ&referrer=%5Bthe%20profile%20of%20Jason%20D.%20Lee%5D(%2Fprofile%3Fid%3D~Jason_D._Lee1) Deployment Efficient Reward-Free Exploration with Linear Function Approximation | OpenReview We study deployment efficient reward-free exploration with linear function approximation, where the goal is to explore a linear Markov Decision Process (MDP)... linear function deployment efficient reward free https://openreview.net/forum?id=h1WGiCxywu Misspecified $Q$-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation... The recent work by Dong and Yang (2023) showed for misspecified sparse linear bandits, one can obtain an $O\left(\epsilon\right)$-optimal policy using a... q learning linear function sparse https://openreview.net/forum?id=ZsvWb6mJnMv Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian |... We present practical and statistically optimal offline RL algorithms under general function approximation and single-policy concentrability. function approximation optimal conservative offline rl https://openreview.net/forum?id=vYZmTEDFoqP Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity... We study reinforcement learning (RL) with linear function approximation under the adaptivity constraint. We consider two popular limited adaptivity models: the... reinforcement learning linear function efficient approximation adaptivity https://openreview.net/forum?id=8WN1GSIJf6U A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning |... We provide a unified framework that nearly includes all model-free and model-based RL classes while maintaining sharp sample efficiency. general framework function approximation sample https://www.iit.edu/events/function-approximation-using-gaussian-kernel Function Approximation Using Gaussian Kernel | Illinois Institute of Technology function approximation gaussian kernel using illinois institute https://deepai.org/publication/alternative-function-approximation-parameterizations-for-solving-games-an-analysis-of-f-regression-counterfactual-regret-minimization Alternative Function Approximation Parameterizations for Solving Games: An Analysis of f-Regression... Dec 6, 2019 - 12/06/19 - Function approximation is a powerful approach for structuring large decision problems that has facilitated great achievements in t... function approximation https://mapleprimes.com/questions/203399-Loop-Ploting-For-Exact-And-Approximation-Function loop ploting for exact and approximation function - MaplePrimes loop exact approximation function https://arxiv.org/abs/math/9909040 [math/9909040] Isomorphisms of function modules, and generalized approximation in modulus Abstract page for arXiv paper math/9909040: Isomorphisms of function modules, and generalized approximation in modulus function modules math isomorphisms https://arxiv.org/abs/2106.11960v1 [2106.11960v1] Variance-Aware Off-Policy Evaluation with Linear Function Approximation Abstract page for arXiv paper 2106.11960v1: Variance-Aware Off-Policy Evaluation with Linear Function Approximation policy evaluation linear function 2106 variance aware https://www.jmlr.org/papers/v26/24-1148.html Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and... deep neural networks https://arxiv.org/abs/2002.01987 [2002.01987] Function approximation by neural nets in the mean-field regime: Entropic... Abstract page for arXiv paper 2002.01987: Function approximation by neural nets in the mean-field regime: Entropic regularization and controlled McKean-Vlasov... https://research.google/pubs/guarantees-for-epsilon-greedy-reinforcement-learning-with-function-approximation/ Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation epsilon greedy reinforcement learning guarantees function approximation https://www.scirp.org/journal/paperinformation?paperid=1889 Design of Hybrid Fuzzy Neural Network for Function Approximation In this paper, a hybrid Fuzzy Neural Network (FNN) system for function approximation is presented. The proposed FNN can handle numeric and fuzzy inputs... fuzzy neural network design of hybrid function approximation https://openreview.net/forum?id=9KEu1ZMeFF&referrer=%5Bthe%20profile%20of%20Francesco%20Orabona%5D(%2Fprofile%3Fid%3D~Francesco_Orabona1) A Finite-Time Analysis of TD Learning with Linear Function Approximation without Projections or... We investigate the finite-time convergence properties of Temporal Difference (TD) learning with linear function approximation, a cornerstone algorithm in the... https://www.mdpi.com/2073-8994/16/12/1568 Parametrized Half-Hyperbolic Tangent Function-Activated Complex-Valued Neural Network Approximation In this paper, we create a family of neural network (NN) operators employing a parametrized and deformed half-hyperbolic tangent function as an activation... hyperbolic tangent function activated complex neural network parametrized half https://openreview.net/forum?id=OD0bQ3F3Nt Improving Real-World Applicability of Networked Mean-Field Games using Function Approximation and... The mean-field game framework can be used to approximate the solutions of games involving very large populations of agents, which is useful in real-world... mean field games https://www.muni.cz/en/research/publications/2367779 Monte Carlo Tree Search with Function Approximation for Risk-constrained Planning and Reinforcement... monte carlo tree search